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    Semisupervised dimensionality reduction and classification through virtual label regression

    Nie, F. and Xu, D. and Li, Xuelong and Xiang, S. (2011) Semisupervised dimensionality reduction and classification through virtual label regression. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) 41 (3), pp. 675-685. ISSN 1083-4419.

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    Abstract

    Semisupervised dimensionality reduction has been attracting much attention as it not only utilizes both labeled and unlabeled data simultaneously, but also works well in the situation of out-of-sample. This paper proposes an effective approach of semisupervised dimensionality reduction through label propagation and label regression. Different from previous efforts, the new approach propagates the label information from labeled to unlabeled data with a well-designed mechanism of random walks, in which outliers are effectively detected and the obtained virtual labels of unlabeled data can be well encoded in a weighted regression model. These virtual labels are thereafter regressed with a linear model to calculate the projection matrix for dimensionality reduction. By this means, when the manifold or the clustering assumption of data is satisfied, the labels of labeled data can be correctly propagated to the unlabeled data; and thus, the proposed approach utilizes the labeled and the unlabeled data more effectively than previous work. Experimental results are carried out upon several databases, and the advantage of the new approach is well demonstrated.

    Metadata

    Item Type: Article
    School: Birkbeck Schools and Departments > School of Business, Economics & Informatics > Computer Science and Information Systems
    Depositing User: Sarah Hall
    Date Deposited: 07 Jun 2013 13:40
    Last Modified: 11 Oct 2016 15:27
    URI: http://eprints.bbk.ac.uk/id/eprint/7407

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